Event Detection Explorer: An Interactive Tool for Event Detection Exploration

TL;DR

ED Explorer is an interactive tool designed to facilitate the exploration and analysis of event detection datasets and models, helping identify issues like bias and imbalance to improve future datasets.

Contribution

The paper introduces ED Explorer, a user-friendly web-based tool for systematic analysis of event detection datasets and models, which was not previously available.

Findings

Identified issues in MAVEN dataset such as sparsity and bias

Demonstrated how ED Explorer aids in dataset analysis and improvement

Provided insights for enhancing event detection datasets

Abstract

Event Detection (ED) is an important task in natural language processing. In the past few years, many datasets have been introduced for advancing ED machine learning models. However, most of these datasets are under-explored because not many tools are available for people to study events, trigger words, and event mention instances systematically and efficiently. In this paper, we present an interactive and easy-to-use tool, namely ED Explorer, for ED dataset and model exploration. ED Explorer consists of an interactive web application, an API, and an NLP toolkit, which can help both domain experts and non-experts to better understand the ED task. We use ED Explorer to analyze a recent proposed large-scale ED datasets (referred to as MAVEN), and discover several underlying problems, including sparsity, label bias, label imbalance, and debatable annotations, which provide us with directions to improve the MAVEN dataset. The ED Explorer can be publicly accessed through http://edx.leafnlp.org/. The demonstration video is available here https://www.youtube.com/watch?v=6QPnxPwxg50.